A dataset that goes over 100GB in size is going to have many many data points, within the millions or even billions ballpark range.

With that many points to process, it doesn’t matter how fast your CPU is, it simply doesn’t have enough cores to do efficient parallel processing.

If your CPU has 20 cores (which would be fairly expensive CPU), you can only process 20 data points at a time!CPUs are going to be better in tasks where clock-speed is more important — or you simply don’t have a GPU implementation.

If there is a GPU implementation for the process you are trying to perform, then a GPU will be far more effective if that task can benefit from parallel processing.

How a Multi-core system can process data faster.

For a single core system (left), all 10 tasks go to a single node.

For the dual-core system (right), each node takes on 5 tasks, thereby doubling the processing speedDeep Learning has already seen its fair share of leveraging GPUs.

Many of the convolution operations done in Deep Learning are repetitive and as such can be greatly accelerated on GPUs, even up to 100s of times.

Data Science today is no different as many repetitive operations are performed on large datasets with libraries like Pandas, Numpy, and Scikit-Learn.

These operations aren’t too complex to implement on the GPU either.

Finally, there’s a solution.

GPU Acceleration with RapidsRapids is a suite of software libraries designed for accelerating Data Science by leveraging GPUs.

It uses low-level CUDA code for fast, GPU-optimized implementations of algorithms while still having an easy to use Python layer on top.

The beauty of Rapids is that it’s integrated smoothly with Data Science libraries — things like Pandas dataframes are easily passed through to Rapids for GPU acceleration.

The diagram below illustrates how Rapids achieves low-level acceleration while maintaining an easy to use top-layer.

fit_predict(X_gpu)Check out the plot of the results from Matplotlib down below:The amount of rises quite drastically when using the GPU instead of CPU.

Even at 10,000 points (far left) we still get a speedup of 4.

54X.

On the higher end of things, with 10,000,000 points we get a speedup of 88.

04X when switching to GPU!Like to learn?Follow me on twitter where I post all about the latest and greatest AI, Technology, and Science!.Connect with me on LinkedIn too!Recommended ReadingWant to learn more about Data Science?.The Python Data Science Handbook book is the best resource out there for learning how to do real Data Science with Python!And just a heads up, I support this blog with Amazon affiliate links to great books, because sharing great books helps everyone!.As an Amazon Associate I earn from qualifying purchases.